System and method for automated odometry calibration for precision agriculture systems

ABSTRACT

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/153,824, filed on Oct. 8, 2018, which is a continuation of U.S.patent application Ser. No. 15/632,583, filed on Jun. 26, 2017, (nowU.S. Pat. No. 10,098,273), which is a continuation of U.S. patentapplication Ser. No. 14/629,361, filed Feb. 23, 2015, (now U.S. Pat. No.9,717,171) which claims the benefit of U.S. Provisional Application No.61/943,158, filed Feb. 21, 2014, each of which is incorporated in itsentirety by this reference. This application is related to U.S. patentapplication Ser. No. 13/788,320 filed Mar. 7, 2013, (now U.S. Pat. No.9,030,549), U.S. patent application Ser. No. 14/329,161, filed Jul. 11,2014, (now U.S. Pat. No. 9,658,201) and U.S. patent application Ser. No.14/444,897, filed Jul. 28, 2014, all of which are incorporated byreference in their entirety for all purposes.

TECHNICAL FIELD

This invention relates generally to the agricultural odometry field, andmore specifically to a new and useful real-time automatic calibration inthe agricultural odometry field.

BACKGROUND

Automatic, real-time plant selection for crop thinning requires precisedistance measurements to ensure complete plant necrosis or retention byaccommodating for the physical offset between the detection mechanismand the action mechanism. However, conventional odometry mechanisms areunsuited for this application. Remote systems, such as GPS, haveresolutions that are too low to determine the precise beginning and endof a plant within a field. Furthermore, these remote systems tend to beslow and power consuming, rendering the systems impractical forreal-time plant selection. Visual odometry mechanisms are too slow foruse in real-time, in-situ plant treatment. On-board systems,particularly mechanical systems, could be ideal due to short computingtimes and low power requirements. However, these systems suffer fromenvironmental interference. For example, a wheel encoder that measuresthe traversed distance based on the radius and number of rotations of awheel rolling along the ground could be used. However, the wheel encodersuffers from interference from the surface features of the ground, whichvaries across a field. As shown in FIG. 4, in soft ground (e.g., soft orloose soil), the wheel of the wheel encoder slips, resulting in slowerwheel rotation than would be expected for the same linear axletranslation. In rough terrain (e.g., dry ground or lumpy soil), thewheel of the wheel encoder traverses over a larger distance (e.g.,surface area) than expected for the same linear axle translation. Thisinaccuracy, while small, can be too large for precision in-situagricultural use, such as when close-packed plants are to be treated.

Thus, there is a need in the agricultural odometry field to create a newand useful automatic odometry calibration system and method. Thisinvention provides such new and useful automatic odometry calibrationsystem and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of the odometry calibration method.

FIG. 2 is a schematic representation of an example of the precisionagriculture system.

FIG. 3 is a schematic representation of the calibration method,including odometry calibration, treatment calibration,

FIG. 4 is a schematic representation of environmental effects on anencoder wheel.

FIG. 5 is a schematic representation of determining pixel shift betweenthe first and second images.

FIG. 6 is a schematic representation of an example of the precisionagriculture system capturing the first image of a first region at afirst time, initiating treatment for a target within the first region ata second time, ceasing the treatment (e.g., transitioning from a firstmode to a second mode) at a third time, and recording the second imageof substantially the first region at a fourth time.

FIG. 7 is a schematic representation of an example of identifying systemfailures based on the treatment correction factors.

FIG. 8 is a schematic representation of an example of the agriculturesystem treating targets in-situ.

FIGS. 9, 10, and 11 are schematic representations of a first, second,and third variant of odometry mechanism placement in a plant field.

FIG. 12 is a schematic representation of a variation of determining thatthe estimated distance has met the predetermined distance.

FIGS. 13 and 14 are a schematic representation of a first and secondexample of the effect differences between the distance estimated by themechanical odometry mechanism, the actual distance determined from thevisual odometry mechanism, and the target distance have on the treatmentapplication.

FIGS. 15, 16, and 17 are schematic representations of a first, second,and third variation of measuring a measured distance of action mechanismoperation in the first mode with a calibration mechanism.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. System

As shown in FIG. 2, the precision agriculture system 100 includes adetection sensor 200, odometry mechanism 300, processing system 400, andframe 500. The system can define a travel axis 102, and is preferablycouplable or include a power source 600. The system can additionallyinclude a treatment mechanism 700 and/or a verification sensor 800. Thesystem can be substantially similar to that disclosed in U.S.application Ser. No. 13/788,320 filed 7 Mar. 2013, incorporated hereinin its entirety by this reference, but can alternatively be any othersuitable system. The precision agriculture system 100 functions toperform the method, and can additionally or alternatively perform anyother suitable functionality. In one variation, the system functions todetermine treatments for and/or apply treatments to plants 12 in-situ(e.g., within the field 20) on a plant-by-plant basis.

The detection sensor 200 (first sensor) of the system functions torecord a first image 222. The first image 222 can be used for plant 12identification, treatment determination, odometry calibration, treatmentcalibration, system monitoring, treatment validation, or be used in anyother suitable manner. The detection sensor 200 can be a camera (e.g.,CCD camera, multispectral camera, stereoscopic camera, omnidirectionalcamera, etc.), ultrasound receiver, or any other suitable sensor. Thedetection sensor 200 can be coupled with one or more emitters configuredto emit an electromagnetic wave (e.g., visible light, UV light, etc.),sound, or any other suitable signal. The detection sensor 200 ispreferably operated to record measurements of the target 10 (e.g., fieldregion within the field of view, plant 12, etc.) at a predeterminedfrequency (e.g., 5 images/second, 5 images/2 seconds, etc.), but canalternatively be recorded in response to the occurrence of a recordationevent (e.g., in response to a time threshold being met, in response to apreceding tactile sensor detecting a plant 12, etc.), or be operated atany other suitable frequency.

The detection sensor 200 is preferably statically mounted to the frame500 (e.g., bolted, screwed, or otherwise mounted to the frame 500), butcan alternatively be movably, rotatably, or otherwise coupled to theframe 500. The intrinsic parameters of the detection camera 220preferably remain substantially constant throughout system operation(e.g., within a threshold variance threshold), but can alternativelydynamically vary throughout system operation. For example, the detectionsensor field of view (FOV) 221 preferably remains constant throughoutsystem operation, but can alternatively be automatically or manuallyadjusted (e.g., to accommodate for changes in camera height 140 ordistance from the imaging target 10 or ground). The number of pixelswithin the FOV is preferably predetermined and known, but canalternatively be unknown, dynamically determined, or determined in anyother suitable manner. In another example, the detection sensor focuslength preferably remains constant throughout system operation, but canalternatively be automatically or manually adjusted during systemoperation. In another example, the image center in the image planepreferably remains static relative to a reference point on the frame 500or statically coupled component thereto, but can alternatively be mobileor be otherwise related to the system.

The odometry mechanism 300 (odometry system) functions to estimate thedistance travelled by the agriculture system 100. The odometry mechanism300 can alternatively determine the distance travelled by theagriculture system 100 (e.g., wherein the travelled distance determinedby the odometry mechanism 300 is within a threshold error of the actualdistance 802 travelled). The odometry mechanism 300 is preferably amechanical odometry mechanism 300, but can alternatively be a visualodometry mechanism 300 or be any other suitable system. The mechanicalodometry mechanism 300 can be a wheel 320 with a rotary encoder 340configured to record full or partial rotations of the wheel 320. Thewheel 320 preferably has a substantially constant diameter, but canalternatively have a variable diameter. The wheel 320 is preferablysubstantially light (e.g., 3 pounds or less), but can alternatively beheavier. The wheel 320 is preferably movably coupled to the frame 500(e.g., coupled along the rotational axis with a 4-bar linkage or anyother suitable linkage that enables vertical and/or lateraltranslation), but can alternatively be statically coupled or otherwisecoupled to the frame 500. The wheel 320 can be configured to trailbehind the detection sensor 200 or agriculture system 100 component,lead before the detection sensor 200 or agriculture system 100component, run alongside the detection sensor 200 or agriculture system100 component, or be arranged in any other suitable relative location.As shown in FIGS. 9, 10, and 11, the wheel 320 can be configured to rollwithin a furrow, along a crop bed, within a wheel track, behind a plowwheel, or arranged in any other suitable location. However, any othersuitable odometry mechanism 300 can be used.

The odometry mechanism 300 is preferably operated to record odometrymeasurements (e.g., wheel revolutions, field scans, etc.) at apredetermined frequency (e.g., 5 measurements/second, 5 measurements/2seconds, 10 measurements/second, etc.), but can alternatively berecorded in response to the occurrence of a recordation event (e.g., inresponse to a time threshold being met, in response to a precedingtactile sensor detecting a plant 12, etc.), or be operated at any othersuitable frequency. The odometry mechanism 300 can estimate the distancetravelled 302 since a reference time, wherein the reference time can bethe first time the system was operated (e.g., wherein the odometrymechanism 300 estimates the total distance travelled over the lifetimeof the agriculture system 100 thus far), the last power-on event (e.g.,wherein the odometry mechanism 300 estimates the distance travelled overthe course of substantially continuous system operation), the last firstimage 222 capture event (e.g., wherein the odometry mechanism 300estimates the distance travelled by the system from the first region224), a reference timestamp associated or unassociated with acalibration event, or any other suitable reference time. Alternatively,the odometry mechanism 300 can estimate the distance from a referencelocation (e.g., a first region 224 associated with the first image 222,starting point, etc.), as determined by a GPS system, triangulation(e.g., cell tower or marker triangulation), or any other suitablelocation determination system. The odometry mechanism 300 measurementscan each be associated with a timestamp (e.g., a global timestamp, arelative timestamp, etc.), a time period, and/or any other suitabletimescale. The odometry mechanism 300 measurements can be stored onboardthe agriculture system 100, stored remotely, or stored in any othersuitable location. The odometry mechanism 300 measurements can beassociated with images (e.g., based on the respective timestamps),unassociated with images, or associated with any other suitable piece ofinformation. However, the odometry mechanism 300 can be operated, andmeasurements used, in any other suitable manner.

The processing system 400 of the system functions to perform the method.Alternatively, some or all of the method can be performed by a remotesystem. Local calibration and/or verification can be preferred in someembodiments to mitigate lag due to remote system processing and datatransfer. However, the method can be performed by any suitablecomponent. In one variation, the processing system 400 functions toselect images for use in the method from a plurality of images, identifytreatment targets 10, determine treatments for the treatment targets 10,treat the targets 10, calibrate the odometry mechanism 300, calibratethe treatment mechanism 700, and monitor the system in near-real time.The processing system 400 can additionally function to discardmeasurements (e.g., images, odometry measurements, etc.) that will notbe used. The processing system 400 can additionally function to learnbased on the recorded images. For example, the processing system 400 canlearn and/or better refine treatment indicator 704 identificationmethods based on the recorded images. The processing system 400 caninclude one or more computing units, one or more storage units, or anyother suitable component.

The frame 500 of the system functions to support the system components.The frame 500 can additionally function to couple the system to a drive,such as a tractor mechanism (e.g., the hitch of a tractor). The frame500 can statically or movably mount the sensor(s) (e.g., detectionsensor 200, verification sensor 800), the odometry mechanism 300, thetreatment mechanisms 700, or any other suitable portion of the system.The frame 500 is preferably substantially rigid, but can alternativelybe jointed, flexible, or have any other suitable construction.

The system preferably defines a travel axis 102 that extends along thedirection of system travel. The travel axis 102 can be substantiallyparallel with the system longitudinal axis, substantially parallel withthe travel path, and/or substantially parallel with the longitudinalaxis or drive axis of the drive mechanism 900.

The system can additionally include a power source 600 configured topower the sensor(s), emitter(s), processing system 400, odometrymechanism 300, treatment mechanism 700, and/or any other suitable systemcomponent. The power source 600 can be a power storage system (e.g., asecondary battery), a generator, a renewable energy system (e.g., asolar panel system), or any other suitable power source 600. The powersource 600 can be arranged on the system (e.g., supported by the frame500), arranged distal the system (e.g., on the drive mechanism 900), orarranged in any other suitable location.

The system can additionally include a treatment mechanism 700 thatfunctions to apply a treatment to the field, more preferably the plants12, in-situ. The treatment mechanism 700 is preferably arranged behindthe detection sensor 200 along the travel axis 102 (e.g., distal a frame500 connection point to the drive mechanism 900), but can alternativelybe arranged in any other suitable location. The treatment mechanism 700can be separated from the detection sensor 200, verification sensor 800,and/or any other suitable component by a sensor-to-treatment mechanismdistance 150. However, the treatment mechanism 700 can be otherwisearranged. The treatment mechanism 700 can be aligned with the travelaxis 102, offset from the travel axis 102, or arranged in any suitablelocation. The system can include one or more treatment mechanisms 700.In one variation, multiple treatment mechanisms 700 are arranged in aline, with a common axis perpendicular the travel axis 102. In a secondvariation, the multiple treatment mechanisms 700 are arranged in anarray. However, the multiple treatment mechanisms 700 can be arranged inany suitable shape or configuration. The treatment mechanisms 700 can benozzles, such as spray nozzles (e.g., fan, cone, jet, etc.), foamsprayers, misters, heat application systems, or any other suitabletreatment mechanisms 700. The treatment (e.g., treatment material,treatment application) can include water, growth promoter (e.g.,fertilizer), growth inhibitor (e.g., fertilizer at high concentrations,alcohol, weed killer, etc.), foam, heat, or any other suitabletreatment. The treatment mechanisms 700 can be lower than the camera(e.g., closer to the ground), higher than the camera (e.g., further fromthe ground), or substantially level with the camera (e.g., wherein thecamera and treatment mechanism 700 ends proximal the ground aresubstantially level when on flat ground). The treatment mechanism 700can be statically mounted to the frame 500, movably mounted to the frame500 (e.g., capable of moving laterally, longitudinally along the travelaxis 102, vertically, or angularly), but can be otherwise mounted to theframe 500. However, the system can include any other suitable treatmentmechanism 700.

The system can additionally include a verification sensor 800 (secondsensor) that functions to record a second measurement 822 (e.g., secondimage) of the measurement pair for odometry calibration, treatmentcalibration, treatment verification, and/or treatment monitoring. Thesecond measurement is preferably of a second region 824, wherein thesecond region 824 can be substantially equal to the first region 224(e.g., overlap beyond a threshold percentage) or be separate anddistinct from the first region 224. The verification sensor 800 can be acamera (e.g., CCD camera, multispectral camera, stereoscopic camera,omnidirectional camera, etc.), ultrasound receiver, or any othersuitable sensor. The verification sensor 800 can be coupled with one ormore emitters configured to emit an electromagnetic wave (e.g., visiblelight, UV light, etc.), sound, or any other suitable signal. The emittercan be the same emitter as that used for the detection sensor 200, or bea separate emitter. The verification sensor 800 is preferably operatedto record measurements of the target 10 (e.g., field region within thefield of view, plant 12, etc.) at a predetermined frequency (e.g., 5images/second, 5 images/2 seconds, etc.), but can alternatively berecorded in response to the occurrence of a recordation event (e.g., inresponse to a time threshold being met, in response to a precedingtactile sensor detecting a plant 12, etc.), or be operated at any othersuitable frequency.

The verification sensor 800 is preferably statically mounted to theframe 500 (e.g., bolted, screwed, or otherwise mounted to the frame500), but can alternatively be movably, rotatably, or otherwise coupledto the frame 500. The verification sensor 800 is preferably arrangedbehind the detection sensor 200, more preferably behind the treatmentmechanism 700 but alternatively in any other suitable position, alongthe travel axis 102. However, the verification sensor 800 can beotherwise arranged. In some variations, the detection sensor 200 toverification sensor 800 distance (sensor or camera separation distance120) is fixed and known because both sensors are statically mounted tofixed, predetermined mounting points in the frame 500. This sensorseparation distance can be automatically determined (e.g., pre-storedbased on the known mounting points), manually determined and storedwithin the system, or otherwise determined. However, the sensor orcamera separation distance 120 can be variable or otherwise configured.

The intrinsic parameters of the verification camera 820 preferablyremain substantially constant throughout system operation (e.g., withina threshold variance threshold), but can alternatively dynamically varythroughout system operation. For example, the verification sensor fieldof view (FOV) 821 preferably remains constant throughout systemoperation, but can alternatively be automatically or manually adjusted(e.g., to accommodate for changes in camera height or distance from theimaging target 10 or ground). The verification camera field of viewpreferably does not overlap with that of the detection sensor 200, butcan alternatively overlap. The number of pixels within the FOV ispreferably predetermined and known, but can alternatively be unknown,dynamically determined, or determined in any other suitable manner. Inanother example, the verification sensor focus length preferably remainsconstant throughout system operation, but can alternatively beautomatically or manually adjusted during system operation. In anotherexample, the image center in the image plane preferably remains staticrelative to a reference point on the frame 500 or statically coupledcomponent thereto, but can alternatively be mobile or be otherwiserelated to the system.

In some variations, the verification sensor 800 is controlled to recordan image of substantially the same geographic region as that of thefirst image 222 (first region 224), as determined based on the distanceestimated by the odometry mechanism 300. Alternatively or additionally,the verification sensor 800 is controlled to substantially continuouslyrecord images, wherein the image substantially corresponding to thefirst region 224 is selected from the plurality of images (e.g., basedon a timestamp correlating the estimated distance from a number ofencoder 340 ticks to the image). However, a second image 822 ofsubstantially the first region 224 can be otherwise obtained.

2. Method

As shown in FIG. 1, the method for operating a precision agriculturesystem includes recording a first image of a first field region S100,estimating a traversal distance for the system with an odometrymechanism S200, recording a second image of a second field region S300,and calibrating the odometry mechanism measurements based on the firstand second images S400. The method functions to leverage visual odometryto correct odometry measurements, more preferably mechanical odometrymeasurements but alternatively measurements from any other suitableodometry mechanism. The method can additionally or alternativelyfunction to increase the precision of treatment application to plantsin-situ (e.g., within a plant field). The method can additionally oralternatively function to monitor system performance and identify systemfailures. The method is preferably performed with the agriculture system100 disclosed above, but can alternatively be performed by any othersuitable system. The method can be performed at a predeterminedfrequency (e.g., substantially continuously), in response to theoccurrence of a calibration event, or at any other suitable frequency.

The method can be used in vegetable precision agriculture (e.g.,weeding, crop thinning, etc.), wherein the method can additionallyinclude treating the plants in-situ based on the odometry calibrationand/or calibrating the treatment mechanism. The method can additionallyor alternatively be used to map crop fields, wherein the odometrycalibration can enable more precise geographical location assignment toplant measurements (e.g., images, multispectral measurements, etc.).However, the method can be otherwise used.

A. Potential Benefits

This method can confer several benefits over conventional agriculturesystems. First, the method can enable high precision treatmentapplication to individual plants (e.g., within error margins of ¼ inchesto ½ inches) using a terrestrial system (e.g., ground-based system).This can be particularly useful in systems with rotary encoders as theodometry mechanism. Rotary encoders can suffer from small encoderdistance variations due to wheel slip and/or sliding along the ground,which can result in treatment errors 160 that are small, but not ofnegligible consequence. By periodically calibrating the odometrymechanism with visual odometry outputs, the method enables both nearreal-time, in-situ distance determination (using the inaccurate but fastrotary encoder system) and high precision distance determination (usingthe more precise but slower visual odometry mechanism). In doing so, themethod provides near real-time odometry and/or treatment calibration fornear real-time feedback to near real-time plant treatment in-situ.

Second, the method can use unknown reference points as the points ofcomparison for the visual odometry mechanism. This enables the system toautomatically calibrate the odometry mechanism and/or treatmentmechanism during system operation (e.g., calibrate the system withoutuser input, in a substantially uncontrolled environment, without a knownreference point). The unknown reference points are preferablyenvironmental features, such as field features (e.g., plants, rocks,shadows, treatment indicators, etc.), but can alternatively be any othersuitable unknown reference point. Alternatively, the system can useknown reference points as the points of comparison for the visualodometry mechanism, such as rulers 15 (e.g., retained a known distanceaway from the camera or the ground, etc.; examples of which are shown inFIGS. 15 and 16), user-placed field markers of known dimension,indicators having contrasting colors (e.g., orange) that are applied tothe field (e.g., colored strips sprayed onto the field), triangulationmarkers (e.g., RF markers placed at known positions within or outside ofthe field), or use any other suitable known reference point.

Third, the method can confer the benefit of utilizing the same image setin multiple ways (e.g., wherein each image or image set has multiplefunctionalities). In particular, the method can use the same image,image pair, or image plurality to identify plants for treatment,calibrate the odometry mechanism, calibrate the treatment mechanism,and/or monitor the system for system failures. The image set canadditionally or alternatively be used in any other suitable manner.

Fourth, some variants of the method can confer the benefit ofconcurrently accommodating for any variable that affects the treatment(e.g., spray) time of flight to the treatment target (e.g., plant).These variables can include treatment mechanism distance from theground, treatment mechanism angle relative to the ground or gravityvector, pressure, treatment mechanism wear (e.g., solenoid sticking), orany other suitable variable. However, the variables can be accommodatedand/or corrected for in any other suitable manner.

In some variations of the method, these benefits can be enabled by largeamounts of on-board processing power capable of performing the methodconcurrently with system operation. These variations have the addedbenefit of mitigating communication delay that can occur with connectedsystems in which all or a portion of the method is performed remotely(e.g., at a remote computing system). Alternatively, the method can beentirely or partially performed remotely (e.g., at a remote computingsystem), wherein raw or processed image information, calibrationparameters, or other information can be communicated via a transmitterand/or receiver.

B. Method Processes

Recording a first measurement of a first field region S100 functions torecord an image for use in odometry calibration. The measurement canadditionally or alternatively be used for target identification, targetprescription (e.g., treatment determination for the plant), treatmentcalibration, or for any other suitable use. The first measurement can bean image (e.g., single image, stereo image, omnidirectional image,multispectral image, etc.), a scan (e.g., a set of linear measurements),or any other suitable measurement. The first measurement is preferablyrecorded with the detection sensor, but can alternatively be recordedwith the verification sensor or any other suitable sensor. The firstmeasurement preferably records parameters of (e.g., an image of) a firstgeographic region within the field (first region, first field region),but can alternatively be associated with any other suitable geographiclocation or region. The first measurement is preferably associated witha timestamp (first time), wherein the timestamp can reflect the time(e.g., absolute or relative) at which the first measurement wasrecorded. Recording the first measurement can additionally includecorrecting the first measurement for known distortions or errors. Forexample, when the measurement is an image, recording the first image caninclude correcting the image for lens distortion using image processingtechniques.

Estimating a traversal distance for the system with an odometrymechanism S200 functions to estimate the distance travelled by thesystem. The traversal distance is preferably estimated from a portion ofthe first region (e.g., from the geographic location corresponding tothe center of the first image, from a geographic location correspondingto an edge of the first image, etc.), wherein the odometry count can bereset after each new first region is identified and recorded, but canalternatively be estimated from a remote reference point (e.g., theoperation starting point) or from any other suitable point. Thetraversal distance can be corrected by the position correction factor orbe uncorrected. The traversal distance is preferably estimated by theodometry mechanism, more preferably by a mechanical odometry mechanism,but alternatively be estimated in any other suitable manner (e.g., usingGPS, etc.). In one example, the mechanical odometry mechanism includes awheel and encoder pair, wherein the wheel rolls along the ground as thesystem traverses along the field, and the encoder measures the number offull and/or partial wheel rotations (e.g., with encoder ticks). However,the distance travelled can be otherwise determined or estimated.

Estimating the traversal distance S200 can additionally include storingthe odometry measurements. The odometry measurements can be storedremotely or on-board. All odometry measurements can be stored.Alternatively, only the odometry measurements corresponding to selectedmeasurements (e.g., measurements by the detection sensor, measurementsby the verification sensor that are selected for use) can be stored.Alternatively, any suitable odometry measurements can be stored. Theodometry measurement can be stored in association with a timestamp, ageographic location, one or more measurements (e.g., images), or inassociation with any other suitable piece of data.

Recording a second measurement of a second field region S300 functionsto record an image for use in odometry calibration. The measurement canadditionally or alternatively be used for target identification, targetprescription (e.g., treatment determination for the plant), treatmentcalibration, treatment validation, treatment monitoring, or for anyother suitable use. The second measurement can be an image (e.g., singleimage, stereo image, omnidirectional image, multispectral image, etc.),a scan (e.g., a set of linear measurements), or any other suitablemeasurement. The second measurement is preferably recorded with theverification sensor (second sensor), but can alternatively be recordedwith the detection sensor (first sensor) or any other suitable sensor.The second measurement preferably records parameters of (e.g., an imageof) a second geographic region within the field (second region, secondfield region), but can alternatively be associated with any othersuitable geographic location or region. The second region is preferablysubstantially the first region, such that it overlaps with the firstregion beyond a threshold percentage or proportion (e.g., 75% of theimage), an example of which is shown in FIG. 5. However, the secondregion can overlap with the first region below a threshold percentage orproportion, be separate and distinct from the first region (e.g., be aregion adjacent the first region, be a region separated from the firstregion by an intervening region, etc.), or be any other suitable region.

The second measurement is preferably associated with a timestamp (secondtime), wherein the timestamp can reflect the time (e.g., absolute orrelative) at which the second measurement was recorded. The second timecan be selected such that the field region within the verificationsensor field of view substantially coincides with the first region, orcan be any other suitable time. In one variation, the second measurementis taken in response to the second time or target travelled distance(e.g., camera separation distance, as estimated by the odometrymechanism) being met. In a second variation, the system can select themeasurement corresponding to the second time from a set of measurementscaptured at a predetermined frequency as the system travels along thefield. However, the second time can be otherwise selected and/or used.

Recording the second measurement S300 can additionally includecorrecting the second measurement for known distortions or errors. Forexample, when the measurement is an image, recording the first image caninclude correcting the image for lens distortion using image processingtechniques.

Calibrating the odometry mechanism based on the first and secondmeasurements S400 functions to reduce the distance measurement errorfrom the odometry mechanism by using a secondary, slower, odometrymechanism. In some variations, calibrating the odometry mechanismincludes correcting mechanical odometry mechanism measurements using avisual odometry mechanism (e.g., wherein the first and secondmeasurement pair includes a first and second image). The odometrymechanism is preferably calibrated based on a plurality of measurementpairs (e.g., wherein each pair includes a first and second measurement),but can alternatively be calibrated based on a single measurement pair,based on a set of first images, based on a set of second images, orbased on any other suitable set of measurements. Calibrating theodometry mechanism S400 can include determining odometry correctionfactors (e.g., a position correction factor and pixels per real-worldinch, etc.) and correcting odometry mechanism measurements with theodometry correction factors.

Determining the odometry correction factors functions to determine thevalues to be used in correcting the odometry measurements. The odometrycorrection factors can accommodate for camera-to-ground heightvariations, which can result in plant size and/or interplant distanceestimation inaccuracies. The odometry correction factors canadditionally or alternatively accommodate for odometry measurementinaccuracies due to ground features, which can result in inaccurateplant location assignment and/or over- or under-treatment of plants. Inone variation, the odometry correction factors can include a cameraheight from the ground or a parameter associated with camera height(e.g., number of pixels per real-world unit of length, such as number ofpixels per real-world inch [PPI]), a position correction factor (e.g., awheel radius correction factor [WR] that accommodates for wheel slip orslide, resulting in an adjusted effective radius), or any other suitableodometry correction factors.

In one example of the method, the odometry correction factors can bedetermined based on an estimated traversed distance between the firsttime and the third time, a pixel shift value between the first andsecond images, and a fixed distance between the first and secondcameras.

The estimated traversed distance can be determined from the odometrymechanism. In one variation in which the system includes a wheel androtary encoder odometry mechanism, the estimated traversed distance canbe determined from the number of full or partial wheel rotations betweenthe first and second time, as measured by the rotary encoder. However,the estimated traversed distance can be determined in any other suitablemanner.

Determining the pixel shift value (PS) can include using featureextraction and correlation techniques to determine the amount of pixelshift 130 between the first and second images. In one example,determining the pixel shift value can include analyzing the first andsecond images to identify a set of common reference features 702 betweenthe first and second images, and determining an amount of pixel shiftbetween the first and second images, based on positions of the commonreference features within the first and second images, respectively. Thepixel shift value can be the number of pixels that the features withinthe first (or second) image should be shifted to substantially match(e.g., within a threshold amount) the same features within the second(or first) image. Alternatively or additionally, the pixel shift valuecan be a vector including a number of pixels and the direction that thefeatures should be shifted. Alternatively or additionally, the pixelshift value can be a percentage of the image size, or be any othersuitable measure of pixel shift. The extracted features can be unknownfeatures, wherein some or all of the parameters of the feature (e.g.,dimensions, size, shape, color, etc.) was not previously known. Examplesof unknown features include environmental or field features (e.g.,in-situ plants, rocks, ground divots, ground protrusions, etc.),treatment application indicators (e.g., spray marks), or any othersuitable feature. Additionally or alternatively, the extracted featurescan be known features, wherein one or more feature parameters are known.In one example, the extracted feature can be a ruler of known dimension(e.g., width, distance between tick marks, etc.) that travels with thesystem a known distance from the camera or a known distance from theground. In a second example, the extracted feature can be a spray markof known dimension having contrasting or non-natural coloring (e.g.,orange, pink, etc.). However, the extracted feature can be any othersuitable pre-existing or manually introduced feature.

In one variation of the method, the odometry correction factors (e.g.,PPI and WR) can be determined based on the following formula:

PS/PPI + N * WR = CC

wherein CC is the camera separation distance (e.g., between the firstand second camera), N is the number of wheel rotations between the firstand second images, PS is the amount of pixel shift between the first andsecond images, PPI is the number of pixels per real-world inch, and WRis the position correction factor and/or effective wheel radiuscorrection factor. The equation is preferably solved based on multiplepixel shift value and wheel rotation pairs, wherein each pair isgenerated from a different image pair. However, the equation can besolved based on a single pixel shift value and wheel rotation pairs,based on a single image, or solved in any other suitable manner.

Determining the odometry calibration factors can additionally includeremoving outliers before determining the odometry calibration factors.Removing outliers can include applying a least-squares method (e.g.,linear or non-linear least squares) to one or more pixel shift value andwheel rotation pairs, then removing outliers (e.g., pixel shift valueand wheel rotation pairs deviating from the model beyond a predetermineddeviation value) from the resultant data set. However, the outliers canbe otherwise removed.

Alternatively or additionally, the odometry mechanism can be calibratedby constructing an optical flow field from the set of images collectedby the detection camera, the set of images collected by the verificationcamera, and/or the set of images collected by both the detection cameraand verification camera, checking the flow field vectors for potentialerrors and to remove outliers, estimating the camera motion from theoptical flow (e.g., by using a Kalman filter, finding geometric and/or3D feature properties and minimizing a cost function based on there-projection error between adjacent images, etc.), and determiningodometry correction factors such that the corrected odometrymeasurements substantially match the camera motion. However, theodometry mechanism can be otherwise calibrated using the visual odometrymechanism. Additionally or alternatively, the system motion can bedetermined based on a “direct” or appearance-based visual odometrytechnique which minimizes an error directly in sensor space, wherein theodometry correction factors can be determined to substantially match thecorrected mechanical odometry measurements to the visualodometry-determined system motion. Additionally or alternatively,calibrating the odometry mechanism can be substantially empirical,wherein the system can iteratively select odometry correction factorvalues, apply the odometry correction factor values to the odometrymechanism measurements, determine the effect of the odometry correctionfactor values on the outcome (e.g., treatment location), and adjust theodometry correction factor values based on the outcome to betterapproximate a desired outcome. However, the odometry mechanism can beotherwise calibrated.

The method can additionally include repeating the odometry calibrationprocess during system operation. The odometry calibration process can berepeated at a predetermined frequency (e.g., continuously during systemoperation), in response to the occurrence of a recalibration event, orat any other suitable frequency. An example of the effects of repeatedodometry calibration is shown in FIG. 17. The recalibration event can bedetermination that an error between an estimated action position and anactual action position (determined from the images) exceeds a thresholderror, or be any other suitable event. The odometry calibration processis preferably performed using a different set of measurements (e.g., aset of measurements more recently recorded), but can alternatively beperformed using an overlapping set of measurements with the prioriteration, the same set of measurements as the prior iteration, or bebased on any other suitable set of measurements.

i. Treatment

As shown in FIG. 3, the method can additionally include treating theplants in-situ S500. The plants can be treated as disclosed in Ser. No.13/788,320 filed 7 Mar. 2013 and/or Ser. No. 14/444,897, filed 28 Jul.2014, but can alternatively be treated in any other suitable manner.Example treatments can include inaction (e.g., not applying a treatmentmaterial to the plant), acting on the plant (e.g., applying a treatmentmaterial to the plant and/or surrounding region), acting on theenvironment surrounding the plant, or include any other suitable planttreatment. The plants are preferably treated between the first andsecond times, during a treatment time period, as the system traversesthrough the field (an example of which is shown in FIG. 8). The plantscan be treated substantially concurrently with, within a threshold timeduration of, before, or after: travelled distance estimation, odometrycalibration, treatment calibration, and/or any other suitable process,an example of which is shown in FIG. 6.

Treating the plants in-situ S500 can include identifying a first plantwithin the first region S510, determining a treatment for the plantS520, determining treatment parameters to achieve the treatment S530,and controlling the treatment mechanism to apply a treatment to thetarget S540. Determining treatment parameters to achieve the treatmentS530 can include determining a target treatment boundary location basedon the first image and determining a transition time to transition thetreatment applied to the field from a first treatment state to a secondtreatment state to achieve a transition location approximating thetarget treatment boundary. The treatment mechanism preferably applies afirst treatment in the first operation mode, and applies a secondtreatment different from the first treatment in the second operationmode. The treatment mechanism can additionally or alternatively applyany suitable number of treatments in any suitable number of modes. Themethod can additionally include identifying a second plant proximal thefirst plant and determining a second treatment for the second plantbefore target treatment boundary determination, wherein the secondtreatment is different from the first plant. However, the plants can betreated in any other suitable manner.

Individual or plant clusters can be identified using feature detectiontechniques, in the manner disclosed in U.S. application Ser. No.13/788,320, or be determined in any other suitable manner. Planttreatments can be determined as disclosed in U.S. application Ser. No.13/788,320 and/or Ser. No. 14/444,897, or be determined in any othersuitable manner. The plant treatment for the first plant preferablycorresponds to the first treatment mode (first treatment state), but canalternatively correspond to any other suitable treatment mode.

Determining a target treatment boundary location based on the firstimage functions to identify a geographic location at which the appliedtreatment should be switched from the first treatment to the secondtreatment to achieve the determined treatments for the plants and/orfield. The target treatment boundary location 710 is preferably arrangedbetween a first and second plant or field region having differentassigned treatments (e.g., target treatments, target outcomes), but canalternatively be arranged in any other suitable location. For example,if a first plant is to be killed and an adjacent, second plant is to beretained, then the target treatment boundary location can be locatedbetween the first and second plants. The target treatment boundarylocation 710 is preferably determined after determining a treatment forthe first plant, and can additionally be determined after determining atreatment for the second plant. The target treatment boundary location710 can be automatically determined (e.g., by maximizing a cost valuefunction, solving an optimization equation, etc.), but can alternativelybe manually or otherwise determined. Alternatively, the target treatmentboundary location 710 can be determined independent of the targettreatments for the plant and/or field, or be otherwise determined.

Determining a transition time functions to determine the time at whichthe treatment mechanism should be actuated to achieve the targettreatment boundary. The transition time is preferably a global time, butcan alternatively be a time duration from a reference time (e.g., fromthe first time) or be any other suitable time. The transition timepreferably accommodates for inaccuracies in the odometry measurement,time of flight delays due to external factors (e.g., wind), internalfactors (e.g., treatment mechanism failures, angle, distance from thetarget, treatment pressure, etc.), or any other suitable inaccuraciesthat can influence the location of the treatment boundary, but canalternatively not accommodate for such inaccuracies.

The transition time can be determined based on the estimated velocity ofthe system and the target distance 706 between an instantaneoustreatment mechanism location and the target treatment boundary location.The estimated velocity of the system can be determined based on the rateof encoder ticks (e.g., the wheel rotation rate), the velocity of thedrive mechanism, or be determined in any other suitable manner. Theestimated velocity of the system can additionally or alternatively becorrected by the position correction factor (e.g., to accommodate groundfeatures) The estimated distance between the instantaneous treatmentmechanism location and the target treatment boundary location can bedetermined based on a known distance between the detection sensormounting point and the treatment mechanism mounting point, or bedetermined in any other suitable manner. The estimated distance betweenthe instantaneous treatment mechanism location and the target treatmentboundary location can be corrected by a sensor- or camera-to-treatmentmechanism correction factor to accommodate for changes in the treatmentmechanism distance from the ground, angle, distance from the detectionsensor, or for any other suitable changes (e.g., due to plants hittingthe treatment mechanisms). The transition time can additionally oralternatively be determined based on a system height, as determined fromthe estimated or determined camera height from the ground, the pixelsper real-world inch, and/or from any other suitable measurement orparameter. The transition time can additionally or alternatively becorrected by a timing delay, wherein the timing delay can be determinedfrom treatment calibration based on a prior treatment. The priortreatment can be within the same field, be the treatment of an adjacentplant, be within the same operation run, or be any other suitabletreatment. However, the transition time can be otherwise determined.

Controlling the treatment mechanism to apply a treatment to the targetS540 can include transitioning the treatment mechanism from a firstoperation mode to a second operation mode and applying a treatment tothe target with the treatment mechanism, or include any other suitablemethod.

Transitioning the treatment mechanism from a first operation mode to asecond operation mode functions to apply the desired treatments to thefield. The treatment mechanism is preferably transitioned at thetransition time, but can alternatively be transitioned at the targettreatment boundary location or be transitioned in response to theoccurrence of any other suitable transition event. The treatmentmechanism can be transitioned before, after, or during treatmentapplication to the target. The treatment mechanism transition ispreferably controlled by the processor, but can alternatively beotherwise controlled.

Applying a treatment to the target with the treatment mechanismfunctions to apply the determined treatment to the plant, field region,or other target. The treatment is preferably applied to previouslyidentified plants concurrently with first plant identification,treatment determination, and/or treatment calibration, but canalternatively be applied before, after, or at any other suitable time.Treatments that can be applied to the target include those disclosed inU.S. application Ser. No. 14/444,897, but can additionally oralternatively include any other suitable treatments. Applying atreatment to the target preferably includes controlling the treatmentmechanism to apply the treatment to the target in one of a plurality ofmodes. For example, the treatment mechanism can spray a treatment in thefirst operation mode, and not spray the treatment in the secondoperation mode. In another example, the treatment mechanism canadditionally spray at a second pressure different from the pressure ofthe first operation mode in a third operation mode. The operation modepreferably corresponds to the selected treatment for the plantcoinciding with the treatment region (e.g., field region receiving thetreatment), but can alternatively be different. In systems includingmultiple treatment mechanisms, applying a treatment to the target canadditionally include selecting one or more treatment mechanisms andcontrolling the selected treatment mechanisms to operate in the selectedmode. The treatment mechanisms can be selected based on the treatmentapplied by the respective treatment mechanisms, the location of thetreatment mechanisms (e.g., proximity to the target), or be selected inany other suitable manner.

The method can additionally include identifying a second plant proximalthe first plant. The second plant can be identified from a seconddetection image (e.g., a second “first image”) recorded by the detectionsensor, or be identified in any other suitable manner. The second plantcan be immediately adjacent the first plant along the travel path,distal (not immediately adjacent) the first plant along the travel path,adjacent the first plant along a vector at an angle to (e.g.,perpendicular, etc.) the travel path, distal the first plant along avector at an angle to (e.g., perpendicular, etc.) the travel path, orarranged in any other suitable location relative to the first plant.

The method can additionally include determining a second treatment forthe second plant different from the first treatment. The secondtreatment preferably corresponds to the second treatment mode (secondtreatment state) different from the first treatment state, but canalternatively correspond to any other suitable treatment mode.

The treatment mechanism is preferably transitioned from the firstoperation mode to the second operation mode after determination of thesecond treatment for the second plant. For example, if the first plantis to be retained and a second plant adjacent the first plant along thetravel axis is to be killed, the system can control the treatmentmechanism to switch from the retention treatment mode (e.g., notspraying, spraying a first fertilizer concentration, spraying a growthpromoter, etc.) to the removal treatment mode (e.g., spraying, sprayinga second fertilizer concentration, spraying a growth inhibitor, etc.) atthe transition time. In contrast, if both the first and second plantsare to be retained, the system can continuously operate in the retentiontreatment mode as it travels by both plants. Alternatively, thetreatment mechanism can be operated in the retention treatment mode onlywhen the treatment mechanism is estimated to be within a thresholddistance of the first plant (e.g., within a threshold distance of thefirst plant before and after the treatment mechanism passes the plant).However, the treatment mechanism can alternatively be transitioned inresponse to the occurrence of any other suitable event.

As shown in FIG. 3, the method can additionally include calibrating thetreatment mechanism S600. Calibrating the treatment mechanism canfunction to accommodate for delays between treatment initiation (e.g.,controlling the treatment mechanism to operate in the desired treatmentmode) and actual treatment application to the target. Calibrating thetreatment mechanism can additionally or alternatively function toaccommodate for changes to the system, such as changes in the system ortreatment mechanism distance from the ground, treatment mechanism anglerelative to a gravity vector or the frame, or any other changes to thesystem that may occur during operation. The treatment mechanism ispreferably calibrated based on the verification image, and canadditionally or alternatively be calibrated based on the detectionimage. The verification image can be the second image used for odometrycalibration or a different image. The verification image can be capturedby the detection sensor, verification sensor, or any other suitablesensor.

Calibrating the treatment mechanism can include identifying treatmentidentifiers within the verification image and determining treatmentcalibration factors based on the parameters of the treatmentidentifiers.

Identifying treatment identifiers within the verification imagefunctions to identify features within the image that are indicative oftreatment application, an example of which is shown in FIG. 12. This canadditionally function to verify whether the treatment occurred S700,and/or whether the treatment was applied to the target. The treatmentindicators can be learned (e.g., based on a trained set) or otherwisedetermined. The treatment identifiers can be identified using featuredetection techniques, or be determined in any other suitable manner.Examples of treatment indicators include color patterns (e.g., a darkband indicating sprayed treatment terminating in a substantially flatboundary, next to a light background indicative of dry soil), heatpatterns, moisture patterns, feature gradient patterns, gradientvectors, foam, or any other suitable feature indicative of appliedtreatment.

Determining the treatment calibration factors function to determinefactors to be used in adjusting the target treatment boundary location,the transition time, and/or any other suitable treatment mechanismoperation parameter. The treatment calibration factors can include atiming delay and a sensor-to-treatment mechanism distance (e.g., camerato treatment mechanism distance), but can additionally or alternativelyinclude any other suitable factor. The treatment calibration factors arepreferably determined based on the set of odometry calibration factors,such as the camera height from the ground (or, alternatively, the PPI)and the position correction factor (determined above), but canalternatively be determined based on any other suitable set of factors.

In one variation of the method, the treatment calibration factors aredetermined using the following formula:

e/PPI = CS − dN * WR * DT

wherein e is the error between the expected or target treatment ortreatment boundary location 710 and the actual treatment or treatmentboundary location 720 and/or the error between the estimated distancetravelled based on the odometry measurement 302 and the actual distancetravelled 802 as determined based on the image pair (examples shown inFIGS. 12, 13, and 14); PPI is the pixels per real-world inch; CS is thecamera-to-treatment mechanism correction factor; dN is the systemvelocity (estimated or determined); WR is the position correctionfactor; and DT is the timing delay. The pixels per real-world inch andposition correction factor can be dynamically determined during odometrycalibration, predetermined (e.g., manually entered), or be otherwisedetermined. The system velocity can be the wheel radial velocity asestimated by the encoder (e.g., rate of encoder ticks), the systemvelocity as determined based on a position sensor (e.g., anaccelerometer), the drive mechanism velocity, or be any other suitableapproximation or determination of the system travel velocity within thefield.

The error between the expected treatment or treatment boundary locationand the actual treatment or treatment boundary location can be in unitsof pixels, real-world measurement (e.g., inches), or in any othersuitable unit. In one variation, the error can be determined byidentifying the target treatment boundary location in the first image,determining the position of the target treatment boundary locationrelative to an identified reference feature within the first image,identifying the treatment indicator indicative of the target treatmentboundary location in the second image (e.g., verification image),identifying the corresponding reference feature in the second image,determining the position of the actual treatment boundary relative tothe reference feature within the second image, and determining thedifference in position (error) between the position of the targettreatment boundary location relative to the reference feature and theactual treatment boundary relative to the reference feature. In a secondvariation, the error can be determined by identifying correspondingreference features within the first and second images, shifting theimages such that the corresponding reference features substantiallycoincide, and comparing the target boundary location determined from thefirst image to the actual boundary location identified within the secondimage. However, the error can be otherwise determined.

The camera-to-treatment mechanism correction factor and timing delay canbe determined by solving the equation. The camera-to-treatment mechanismcorrection factor and timing delay can be determined based on one ormore error-system velocity pairs. The first and second images can becollected at a single system velocity value (e.g., such that thecorrection factors are applicable to treatment adjustment at thevelocity value), at multiple system velocity values (e.g., such that thecorrection factors are applicable to treatment adjustment at multiplevelocity values), or be determined based on any other suitable set ofmeasurements collected in association any other suitable set ofparameter values. Alternatively, the camera-to-treatment mechanismcorrection factor and/or timing delay can be determined in any othersuitable manner.

The method can additionally include repeating the treatment calibrationprocess during system operation. The treatment calibration process canbe repeated at a predetermined frequency (e.g., continuously duringsystem operation), in response to the occurrence of a recalibrationevent, or at any other suitable frequency. An example of the effects ofrepeated treatment calibration is shown in FIG. 17. The recalibrationevent can be determination that an error between an estimated actionposition and an actual action position (determined from the images)exceeds a threshold error, or be any other suitable event. The treatmentcalibration process is preferably performed using a different set ofmeasurements (e.g., a set of measurements more recently recorded), butcan alternatively be performed using an overlapping set of measurementswith the prior iteration, the same set of measurements as the prioriteration, or be based on any other suitable set of measurements.

ii. Monitoring

As shown in FIG. 3, the method can additionally include monitoring thesystem S800, which can function to identify system failures, treatmentmechanism failures, or failure of any other suitable system component orfunctionality. The system can send a notification to the drive mechanism(e.g., to a user interface within the cab of the tractor), send anotification to a remote system (e.g., remote monitoring system), storean error message on-board the system, or perform any other suitableaction in response to identification of a failure. Examples of failuresinclude solenoid sticking (e.g., failure on or failure off),overtreatment (e.g., treatment of adjacent regions or plants),undertreatment (e.g., treatment of only a portion of the region or plantto be treated), dripping, incorrect treatment mechanism operation, orany other suitable failure.

The system is preferably monitored (e.g., self-monitored, manuallymonitored, or remotely monitored) based on a monitoring image. Themonitoring image can be the second image used for odometry calibration,the verification image, or any other suitable image. The monitoringimage can be recorded by the detection sensor, verification sensor, orany other suitable sensor. However, the system can be monitored in anyother suitable manner.

In a first variation, monitoring the treatment mechanism can include:comparing a first set of treatment correction factors to a plurality oftreatment correction factor sets, and determining a treatment mechanismfailure in response to the first set of treatment correction factorsdeviating from the plurality beyond a threshold deviation. For example,a least-squares method (e.g., linear or non-linear least squares) can beapplied to the plurality of treatment correction factors sets, whereindeviants from the best fit equation can be identified as outliers (anexample of which is shown in FIG. 7). These outliers can be identifiedas treatment mechanism errors, and can additionally or alternatively beremoved from the plurality of treatment correction factor sets used todetermine the treatment correction factors for use in treatmentcalibration. An error for the timestamp and/or geographic locationassociated with the first or second image of the first image pair canadditionally be generated if the first set of treatment correctionfactors qualify as an outlier. The treatment correction factors can bedetermined during treatment calibration or be determined in any othersuitable manner. The treatment correction factors can include acamera-to-treatment mechanism distance correction factor value and atiming delay value, and can additionally or alternatively include anyother suitable correction factor. The first set of treatment correctionfactors is preferably extracted from a first image pair, while theplurality of treatment correction factor sets can be extracted from aplurality of image pairs recorded before and/or after to the first imagepair.

In a second variation, a failure can be detected in response toidentification of a difference between the target treatment boundarylocation and the actual treatment boundary location (e.g., the errordetermined during treatment calibration) exceeding a thresholddifference. The threshold difference can be ½ an inch, ¼ of an inch, orbe any other suitable distance. However, a failure can be identified inresponse to determination that the treatment mechanism is continuouslytreating (e.g., failed on, as determined from the verification ormonitoring image), has failed to treat (e.g., failed off, as determinedfrom the verification or monitoring image), is dripping or otherwiseapplying treatment between active treatment operation (e.g., asdetermined from the verification or monitoring image), has entirely orpartially treated a second plant with treatment intended for a firstplant, has failed to treat all or a portion of the region or plant to betreated, in response to determination that more than a threshold numberof plants have been mistreated (e.g., have had the wrong treatmentapplied to them), in response to determination that an incorrecttreatment mechanism was operated (e.g., based on the location of thetreatment indicators within the verification or monitoring image), or inresponse to detection of any other suitable failure condition. Thefailure conditions identified from the images can be learned (e.g.,using machine learning techniques), manually identified, or otherwiseidentified.

Additionally or alternatively, the image pairs and/or correction factorscan be used to dynamically, iteratively, or otherwise build a binarymodel, build a learning plant model (e.g., record plant parametervalues), used as a binarization check, used to learn environmentalparameters (e.g., learn or otherwise determine lighting and/or imagecharacteristics for later analyses), used to characterize the ambientenvironment (e.g., used to determine dry soil, wet soil, etc.), or usedin any other suitable manner. The binary model can include arecommendation system and/or automatic plant selection system, whereindifferent plant combinations can be analyzed against the binary moduleduring the calibration.

An alternative embodiment preferably implements the above methods in acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with a precision agricultural treatment mechanism.The precision agricultural treatment mechanism can include a mechanicalodometry mechanism, a visual odometry mechanism, and an odometrycalibration system configured to use the slower visual odometrymechanism outputs to calibrate the faster mechanical odometry mechanismoutputs. The precision agricultural treatment mechanism can additionallyinclude a treatment mechanism and a treatment calibration systemconfigured to leverage the visual odometry mechanism inputs (e.g.,images) and physical system outputs to identify targets (e.g., plants orfield regions) for treatment, correct for treatment mechanisminaccuracies, and substantially precisely treat the targets (e.g., basedon the images and odometry calibration). The treatment calibrationsystem outputs and/or visual odometry mechanism inputs (e.g., images)can additionally be used to verify target treatment, monitor theprecision agricultural treatment mechanism for failures, and/or be usedin any other suitable manner. The computer-readable medium may be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component ispreferably a processor but the instructions may alternatively oradditionally be executed by any suitable dedicated hardware device.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

1. (canceled)
 2. A method for calibrating a treatment mechanismphysically mounted to a farming machine using a sensor physicallymounted to the farming machine as the farming machine operates within afield, the method comprising: capturing, by the sensor, a first image ofa first plant in the field, the first image comprising featuresindicative of a first treatment applied to the first plant by thetreatment mechanism; determining, with a computer and based on the firstimage, a first timing delay between when the treatment mechanism wasactuated to dispense the first treatment and when the first treatmentwas applied to the first plant by the treatment mechanism; capturing, bythe sensor, a second image of a second plant in the field, the secondimage comprising features indicative of a second treatment applied tothe second plant by the treatment mechanism; determining, by thecomputer and based on the second image, a second timing delay betweenwhen the treatment mechanism was actuated to dispense the secondtreatment and when the second treatment was applied to the second plantby the treatment mechanism; and responsive to determining a deviationbetween the first timing delay and the second timing delay, calibratingthe treatment mechanism based on the deviation.
 3. The method of claim2, wherein the treatment mechanism is a spray implement and the firsttiming delay and the second timing delay represent delays in actuationof the spray implement and when the spray is applied to the plant. 4.The method of claim 2, further comprising: responsive to determiningthat the second timing delay deviates from the first timing delay bymore than a threshold deviation, identifying a failure of the treatmentmechanism; communicating a notification of the identified failure to aremote monitoring system; and halting operation of the treatmentmechanism.
 5. The method of claim 2, further comprising: determining,based on the first image, a first treatment calibration factorrepresenting a first distance between the treatment mechanism and thesensor; determining, based on the second image, a second treatmentcalibration factor representing a second distance between the treatmentmechanism and the sensor; and responsive to determining an additionaldeviation between the first treatment calibration factor and the secondtreatment calibration factor, calibrating the treatment mechanism basedon the second treatment calibration factor.
 6. The method of claim 2,wherein the first and second image include image data representing atarget treatment location and an actual treatment location of the firsttreatment, the method further comprising: determining, based on thefirst image, a first treatment calibration factor correcting for a firstdistance between the target treatment location and the actual treatmentlocation as measured in the first image; determining, based on thesecond image, a second treatment calibration factor correcting for asecond distance between the target treatment location and an actualtreatment location as measured in the second image; and responsive todetermining an additional deviation between the first treatmentcalibration factor and the second treatment calibration factor,calibrating the treatment mechanism based on the second treatmentcalibration factor.
 7. The method of claim 2, further comprising:determining a first set of treatment calibration factors based on thefirst image and a second set of treatment calibration factors based onthe second image; comparing the first set and the second set oftreatment calibration factors to identify outliers representingcalibration errors, each identified outlier representing deviationsbetween the first set and the second set of treatment calibrationfactors; and calibrating the treatment mechanism based on thecalibration errors represented by the identified outliers.
 8. The methodof claim 7, further comprising: characterizing deviations between thefirst timing delay and the second timing delay based on the comparisonof the first set of treatment factors and the second set of treatmentfactors; and wherein deviations exceeding a threshold deviationrepresent an error in actuation of the treatment mechanism.
 9. Anon-transitory computer readable storage medium storing instructions forcalibrating a treatment mechanism physically mounted to a farmingmachine that, when executed by a processor, cause the processor to:capture, by the sensor, a first image of a first plant in the field, thefirst image comprising features indicative of a first treatment appliedto the first plant by the treatment mechanism; determine, with acomputer and based on the first image, a first timing delay between whenthe treatment mechanism was actuated to dispense the first treatment andwhen the first treatment was applied to the first plant by the treatmentmechanism; capture, by the sensor, a second image of a second plant inthe field, the second image comprising features indicative of a secondtreatment applied to the second plant by the treatment mechanism;determine, by the computer and based on the second image, a secondtiming delay between when the treatment mechanism was actuated todispense the second treatment and when the second treatment was appliedto the second plant by the treatment mechanism; and responsive todetermining a deviation between the first timing delay and the secondtiming delay, calibrate the treatment mechanism based on the deviation.10. The non-transitory computer readable storage medium of claim 9,further comprising instructions that cause the processor to: responsiveto determining that the second timing delay deviates from the firsttiming delay by more than a threshold deviation, identify a failure ofthe treatment mechanism; communicate a notification of the identifiedfailure to a remote monitoring system; and halt operation of thetreatment mechanism.
 11. The non-transitory computer readable storagemedium of claim 9, further comprising instructions that cause theprocessor to: determine, based on the first image, a first treatmentcalibration factor representing a first distance between the treatmentmechanism and the sensor; determine, based on the second image, a secondtreatment calibration factor representing a second distance between thetreatment mechanism and the sensor; and responsive to determining anadditional deviation between the first treatment calibration factor andthe second treatment calibration factor, calibrate the treatmentmechanism based on the second treatment calibration factor.
 12. Thenon-transitory computer readable storage medium of claim 9, wherein thefirst and second image include image data representing a targettreatment location and an actual treatment location of the firsttreatment, and further comprising instructions that cause the processorto: determine, based on the first image, a first treatment calibrationfactor correcting for a first distance between the target treatmentlocation and the actual treatment location as measured in the firstimage; determine, based on the second image, a second treatmentcalibration factor correcting for a second distance between the targettreatment location and an actual treatment location as measured in thesecond image; and responsive to determining an additional deviationbetween the first treatment calibration factor and the second treatmentcalibration factor, calibrate the treatment mechanism based on thesecond treatment calibration factor.
 13. The non-transitory computerreadable storage medium of claim 9, further comprising instructions thatcause the processor to: determine a first set of treatment calibrationfactors based on the first image and a second set of treatmentcalibration factors based on the second image; compare the first set andthe second set of treatment calibration factors to identify outliersrepresenting calibration errors, each identified outlier representingdeviations between the first set and the second set of treatmentcalibration factors; and calibrate the treatment mechanism based on thecalibration errors represented by the identified outliers.
 14. Thenon-transitory computer readable storage medium of claim 13, furthercomprising: characterize deviations between the first timing delay andthe second timing delay based on the comparison of the first set oftreatment factors and the second set of treatment factors; and whereindeviations exceeding a threshold deviation represent an error inactuation of the treatment mechanism.
 15. A farming machine comprising:a sensor mounted to the farming machine; a treatment mounted to thefarming machine; a processor; and a non-transitory computer readablestorage medium storing instructions for calibrating a treatmentmechanism physically mounted to a farming machine that, when executed bya processor, cause the processor to: capture, by the sensor, a firstimage of a first plant in the field, the first image comprising featuresindicative of a first treatment applied to the first plant by thetreatment mechanism; determine, with a computer and based on the firstimage, a first timing delay between when the treatment mechanism wasactuated to dispense the first treatment and when the first treatmentwas applied to the first plant by the treatment mechanism; capture, bythe sensor, a second image of a second plant in the field, the secondimage comprising features indicative of a second treatment applied tothe second plant by the treatment mechanism; determine, by the computerand based on the second image, a second timing delay between when thetreatment mechanism was actuated to dispense the second treatment andwhen the second treatment was applied to the second plant by thetreatment mechanism; and responsive to determining a deviation betweenthe first timing delay and the second timing delay, calibrate thetreatment mechanism based on the deviation.
 16. The farming machine ofclaim 15, further comprising instructions that cause the processor to:responsive to determining that the second timing delay deviates from thefirst timing delay by more than a threshold deviation, identify afailure of the treatment mechanism; communicate a notification of theidentified failure to a remote monitoring system; and halt operation ofthe treatment mechanism.
 17. The farming machine of claim 15, furthercomprising instructions that cause the processor to: determine, based onthe first image, a first treatment calibration factor representing afirst distance between the treatment mechanism and the sensor;determine, based on the second image, a second treatment calibrationfactor representing a second distance between the treatment mechanismand the sensor; and responsive to determining an additional deviationbetween the first treatment calibration factor and the second treatmentcalibration factor, calibrate the treatment mechanism based on thesecond treatment calibration factor.
 18. The farming machine of claim15, wherein the first and second image include image data representing atarget treatment location and an actual treatment location of the firsttreatment, further comprising instructions that cause the processor to:determine, based on the first image, a first treatment calibrationfactor correcting for a first distance between the target treatmentlocation and the actual treatment location as measured in the firstimage; determine, based on the second image, a second treatmentcalibration factor correcting for a second distance between the targettreatment location and an actual treatment location as measured in thesecond image; and responsive to determining an additional deviationbetween the first treatment calibration factor and the second treatmentcalibration factor, calibrate the treatment mechanism based on thesecond treatment calibration factor.
 19. The farming machine of claim15, further comprising instructions that cause the processor to:determine a first set of treatment calibration factors based on thefirst image and a second set of treatment calibration factors based onthe second image; compare the first set and the second set of treatmentcalibration factors to identify outliers representing calibrationerrors, each identified outlier representing deviations between thefirst set and the second set of treatment calibration factors; andcalibrate the treatment mechanism based on the calibration errorsrepresented by the identified outliers.
 20. The system of claim 2,wherein the treatment mechanism is a spray implement and the firsttiming delay and the second timing delay represent delays in actuationof the spray implement and when the spray is applied to the plant.